All growth-stage companies go through a challenging phase of growth. A few can find the next product + target market that shifts their growth curve. Some well-known B2B examples are Segment, HubSpot, etc. While others get stuck in linear growth, for example, hundreds of mar-tech companies have delivered linear growth at best. The key difference between the two scenarios is a company’s ability to fire more shots and learn about growth.
Developing and launching new products are exciting for a company. When successful, a new product launch promises not only revenue growth but also many other benefits such as increased market leadership or competitive advantage, highly engaged customers, etc. Although, teams often treat a new product launch as a silver bullet solution. Whereas, real growth is most likely a sum of several shots fired. Much of it comes down to how a new product is developed and launched.
Traditional product development and launch is risky and offers limited learning for growth due to linear thinking
In the B2B models, a new product is generally defined as a product that will tap into the new budgets of existing and new customers. One of our clients has been a market leader for over a decade offering B2B ad products, tapping into the “open web” marketing budgets of their users. They needed to find new markets for growth and tried tapping into the “social media” marketing budget. Since the company offered high ACV (Average Customer Value) products (typically over $50K), the rest of the organization believed that every new product would bring a similar revenue impact. Such an organizational belief had set a very high expectation on the new product launch. While the company may have got lucky, it was not possible to convert the new product launch into high ACV sales overnight.
Companies typically have a new product development process that looks like this:
The traditional approach to new product development and launch is not wrong but risky. In the traditional approach, teams primarily focus only on generating new sales. All the different steps that a new product team has to go through are designed to be finished so that the sales teams can sell the product as quickly as possible. This process lacks valuable learning along the way. So whether it works for one time or whether it doesn’t work at all, the teams neither know how to repeat success nor what to improve the next time around.
A growth mindset requires firing more shots to learn and refine new product strategy
Today tools and technologies offer a better approach. Companies need continuous experimentations to learn and refine launches to ensure meeting growth goals. Generally, product and engineering teams are designed to conduct the R&D and development experiments. PMs are used to set up experiments for the new user experiences and engineers and scientists are used to trying different math, partner APIs, etc. In addition to these, teams need experiments for acquisition (starting ideally with their current customers), onboarding (ability to quickly deliver value), etc. A modern product development and launch process look like this:
The new process delivers the learning to unlock the value of the new products throughout the launch process with the ultimate goal of creating a meaningful impact on sales. In this approach, teams are required to collect data (learning) and connect the dots across marketing campaigns, sales tactics, onboarding, CRM, etc. These experiments are the multiple shots that lead to a repeatable and sustainable growth process.
New product development and launch requires clearly defined goals
A strategy is an explicit set of choices, which when chosen guides the stated assumptions and experiments. A simple example of a goal and potential strategies are:
Increase total revenue:
Choice A: Acquire new customers
Create awareness among buyers/customers
Target across buyers’ journey
Choice B: Sell to current customers
We explicitly chose to target current customers, “adjacent buyers” for our B2B ad-tech client. This helped us to not dilute our focus on new customers or elsewhere. One key assumption was that the adjacent buyers and users were leveraging data/insights from the core product. Another assumption was that these adjacent buyers valued performance over anything else since delivering higher social media ads ROI was one of their intrinsic motivations. So we ran R&D experiments to build an MVP (minimal viable product) of a new social media marketing product that leveraged insights from the core product. It helped unlock the value for the adjacent buyers and users by our client. These assumptions also led to the pricing strategy - we tested only performance-based pricing (we got paid only if we deliver better performance).
This process led to the following learning and results:
The sales process became much faster since users paid for performance and removed the friction of pricing, budget approvals, etc.
It was easier to generate awareness among the adjacent users due to already known insights from the core product
Tapping social media ad budgets is hard due to the very high noise in the industry with thousands of vendors. Our focus on pay for performance helped cut through the noise.
Each growth iteration provides crucial learning to increase the chance of success
Based on learning from a period (quarter or a few months) or iteration, the strategy, assumptions, and experiments get continuously refined. The follow-on may be to go deeper with the existing target buyers or to expand the target buyers. At each iteration, the learning from previous iterations increases the chance of success. Additionally, from a financial risk standpoint, a typical product team of 4-5 people costs at least $1M in a year, plus the cost of launch and sales of the new product.
For most companies, it means a significant level of investment. The new product approach further reduces that financial risk by breaking down the process into multiple, shorter iterations of learning and goal delivery. In our experience, each iteration from R&D to product launch should cost around $100K - $150K. So at any point, an executive or a team can change course or kill the product altogether based on real data-driven insights and focus on other promising ideas and markets.